{"ID":2829942,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.11767","arxiv_id":"2512.11767","title":"Learning Minimal Representations of Fermionic Ground States","abstract":"We introduce an unsupervised machine-learning framework that discovers optimally compressed representations of quantum many-body ground states. Using an autoencoder neural network architecture on data from $L$-site Fermi-Hubbard models, we identify minimal latent spaces with a sharp reconstruction quality threshold at $L-1$ latent dimensions, matching the system's intrinsic degrees of freedom. We demonstrate the use of the trained decoder as a differentiable variational ansatz to minimize energy directly within the latent space. Crucially, this approach circumvents the $N$-representability problem, as the learned manifold implicitly restricts the optimization to physically valid quantum states.","short_abstract":"We introduce an unsupervised machine-learning framework that discovers optimally compressed representations of quantum many-body ground states. Using an autoencoder neural network architecture on data from $L$-site Fermi-Hubbard models, we identify minimal latent spaces with a sharp reconstruction quality threshold at...","url_abs":"https://arxiv.org/abs/2512.11767","url_pdf":"https://arxiv.org/pdf/2512.11767v1","authors":"[\"Felix Frohnert\",\"Emiel Koridon\",\"Stefano Polla\"]","published":"2025-12-12T18:26:05Z","proceeding":"quant-ph","tasks":"[\"quant-ph\",\"cond-mat.str-el\",\"cs.LG\"]","methods":"[]","has_code":false}